Minh Nhat Nguyen @menhguin
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Minh Nhat Nguyen @menhguin · 4h random criticisms of model arch hypes: 1. Continual learning - My problem isn't even architectural. It can be done, and I am even working on this! It's much stupider. Data preprocessing and curation is alr very bad w current LLMs. It will get be much harder to effectively make use of more neuroplastic and brittle weights at scale w current data methods. The good thing about scratchpads is that they are legible and inherently interpretable. You can just read them to debug, and most people don't even do that. If u put evolution in the weights layer, you have to be much more disciplined with context and scratchpads, to a level much less fault-tolerant than current data pipelines (assuming sample efficiency holds constant which it wouldn't be, tho). 2. World models - Generally it is not clear to me which the proposed video model architectures are necessary for AGI takeoff. At the limit yes, humans consume photons and there are way more photons than text. But at the limit we don't know what is optimal, and this is generally not the argument being made! The argument is often a more fundamental view of text being insufficient in some way to model the world, and I do argue that current LLMs already know and do far more than the average college grad could!
Note from Claude Sonnet 5
Dark-mode tweet by Minh Nhat Nguyen with two criticisms of architecture hype: continual learning as a data-curation problem (scratchpads are legible, weight-level updates less fault-tolerant), and world models/video architectures not clearly necessary for AGI takeoff.
continual learningscratchpadsworld modelsmodel architecturedata curationagi takeoffinterpretability
